Peer-reviewed article
Application of machine learning to understand child marriage in India
Anita Raj, Nabamallika Dehingia, Abhishek Singh, Lotus McDougal, Julian McAuley ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study used machine learning methods applied to India's nationally-representative health survey data to identify factors associated with child marriage. The analysis found that machine learning models were more accurate than traditional regression methods and identified known risk factors like fertility and socioeconomic vulnerability, but also revealed a previously under-studied connection to low use of maternal and infant nutrition services. The findings suggest that targeted public health outreach for nutrition programs could be part of efforts to address child marriage in India.
Key points
- India has the highest number of girls married as minors globally, with increasing social and health risks
- Machine learning models demonstrated better accuracy and lower error rates compared to traditional logistic regression
- Regularized models confirmed known correlates: higher fertility, longer marriage duration, geographic and socioeconomic vulnerabilities
- Novel finding: non-utilization of health system benefits related to nutrition for mothers and infants was associated with child marriage
- The iterative thematic approach identified variables not previously focused upon in child marriage research
Limitations
This is a cross-sectional analysis of retrospective data, so it cannot establish causal relationships or temporal ordering. The study identifies correlates of child marriage but does not evaluate any intervention to prevent or reduce the practice.
Abstract
BACKGROUND: Prior research documents that India has the greatest number of girls married as minors of any nation in the world, increasing social and health risks for both these young wives and their children. While the prevalence of child marriage has declined in the nation, more work is needed to accelerate this decline and the negative consequences of the practice. Expanded targets for intervention require greater identification of these targets. Machine learning can offer insight into identification of novel factors associated with child marriage that can serve as targets for intervention. METHODS: We applied machine learning methods to retrospective cross-sectional survey data from India on demographics and health, the nationally-representative National Family Health Survey, conducted in 2015-16. We analyzed data using a traditional regression model, with child marriage as the dependent variable, and 4000+ variables from the survey as the independent variables. We also used three commonly used machine learning algorithms- Least Absolute Shrinkage and Selection Operator (lasso) or L-1 regularized logistic regression models; L2 regularized logistic regression or ridge models; and neural network models. Finally, we developed and applied a novel and rigorous approach involving expert qualitative review and coding of variables generated from an iterative series of regularized models to assess thematically key variable groupings associated with child marriage. FINDINGS: Analyses revealed that regularized logistic and neural network applications demonstrated better accuracy and lower error rates than traditional logistic regression, with a greater number of features and variables generated. Regularized models highlight higher fertility and contraception, longer duration of marriage, geographic, and socioeconomic vulnerabilities as key correlates; findings shown in prior research. However, our novel method involving expert qualitative coding of variables generated from iterative regularized models and resultant thematic generation offered clarity on variables not focused upon in prior research, specifically non-utilization of health system benefits related to nutrition for mothers and infants. INTERPRETATION: Machine learning appears to be a valid means of identifying key correlates of child marriage in India and, via our innovative iterative thematic approach, can be useful to identify novel variables associated with this outcome. Findings related to low nutritional service uptake also demonstrate the need for more focus on public health outreach for nutritional programs tailored to this population.